Machine learning assisted reservoir simulation

ABSTRACT

An embodiment includes a method for use by at least one machine learning classifier. The method comprises the machine learning classifier obtaining one or more recent results from at least one geomechanical simulation; the machine learning classifier comparing the recent results to stored historical data; and, based on the comparing, the machine learning classifier deciding at least one reservoir model for use by at least one reservoir simulation.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/197,734 file Jun. 29, 2016, the complete disclosure of which isexpressly incorporated herein by reference in its entirety for allpurposes.

FIELD OF THE INVENTION

The present invention relates to the electrical, electronic and computerarts, and, more particularly, to geomechanical simulation of areservoir.

BACKGROUND OF THE INVENTION

One of the most challenging problems in the petroleum industry is theunderstanding and prediction of subsidence at the surface due toformation compaction that happens as a result of fluid withdrawal fromthe reservoir. In some oil fields (e.g., poorly compacted reservoirs),stress changes associated with reservoir compaction can have beneficialresults on fluid recovery (e.g., oil and gas production).

However, reservoir compaction may also reduce permeability, causingsurface subsidence and damaging well equipment. Subsidence phenomena cancause excessive stress at the well casing, which can result in casingbuckling and/or casing sheer.

Subsidence phenomena can also cause excessive stress within thecompletion zone, where collapse of structural integrity could lead toloss of production (e.g., due to pressure decline). Subsurfacesubsidence can result in problems at the wellhead or with pipelinesystems and platform foundations. Mudline subsidence can cause faultactivation or movement, which in turn can result in reduced wellborestability (e.g., due to concrete cracking) or subsea wellhead failures.Open or closed fractures can occur in a production well or an injectionwell, or along a production length or an injection length.

Progressive activation of faults and fractures affect phenomena such asstress arching and a nonlinear stress path. Unlike standard compactiondrive simulation, there is no simple linear method to account for theeffects of stress on permeability, especially for fractured systems, inwhich the changes of permeability might be directional, localized, andstrongly nonlinear. As a result, fluid flow in a porous medium undersuch scenarios cannot be simplified to compressibility or pressuredependent porosity/permeability changes. Modeling of such processes isachieved by incorporation of geomechanical effects resulting from fluidflow in the porous medium.

Thus, many applications in the petroleum industry require modeling ofboth the porous flow of reservoir fluids (reservoir simulation) and ofmechanical deformation caused by reservoir stresses and displacements(geomechanical simulation) to produce realistic results of reservoirsunder production and especially to simulate the behavior of naturallyfractured reservoirs. For example, reservoir simulation coupled withgeomechanical simulation is used to model reservoir fluid flows andphysical phenomena such as compaction, subsidence, induced fracturing,enhancement of natural fractures and/or fault activation.

This coupling may be implemented using an algorithm in which informationis exchanged between a reservoir simulator and a geomechanical simulatorin an iterative, staggered manner. The coupling of geomechanical andreservoir simulations in hydrocarbon or gas reservoir production inducesvariations in time and space of reservoir pressure, saturation andtemperature. In turn, changes in thermal and hydraulic reservoirproperties may cause a modification of the stress state in and aroundthe reservoir. The stress changes may then alter the reservoir fluidflow parameters and then the reservoir production scenario.

In conventional approaches, a reservoir model is selected, and then thatreservoir model is utilized for the coupling (e.g., the model isutilized by both a reservoir simulator and a geomechanical simulator).Once a reservoir model is selected, then the coupling (e.g., bothreservoir simulation and geomechanical simulation) must be fullyimplemented using only that model. Once coupling is completed using theselected model, if the coupling using the selected model leads tounsatisfactory results, the selected model is abandoned and a new (e.g.,more complex) model is selected instead. Coupling must then be fullyre-implemented using the new model. For example, after coupling (e.g.,both reservoir simulation and geomechanical simulation) is completedusing a “single porosity” model, if the results are unsatisfactory, the“single porosity” model is replaced with a more complex “dual porosity”model and the entire coupling process (e.g., both reservoir simulationand geomechanical simulation) is repeated using the new “dual porosity”model.

SUMMARY OF THE INVENTION

Principles of the invention provide techniques for use by at least onemachine learning classifier. An illustrative embodiment of the presentinvention includes the machine learning classifier obtaining one or morerecent results from at least one geomechanical simulation; the machinelearning classifier comparing the recent results to stored historicaldata; and, based on the comparing, the machine learning classifierdeciding at least one reservoir model for use by at least one reservoirsimulation.

As used herein, “facilitating” an action includes performing the action,making the action easier, helping to carry the action out, or causingthe action to be performed. Thus, by way of example and not limitation,instructions executing on one processor might facilitate an actioncarried out by instructions executing on a remote processor, by sendingappropriate data or commands to cause or aid the action to be performed.For the avoidance of doubt, where an actor facilitates an action byother than performing the action, the action is nevertheless performedby some entity or combination of entities.

One or more embodiments of the invention or elements thereof can beimplemented in the form of a computer program product including acomputer readable storage medium with computer usable program code forperforming the method steps indicated. Furthermore, one or moreembodiments of the invention or elements thereof can be implemented inthe form of a system (or apparatus) including a memory, and at least oneprocessor that is coupled to the memory and operative to performexemplary method steps. Yet further, in another aspect, one or moreembodiments of the invention or elements thereof can be implemented inthe form of means for carrying out one or more of the method stepsdescribed herein; the means can include (i) hardware module(s), (ii)software module(s) stored in a computer readable storage medium (ormultiple such media) and implemented on a hardware processor, or (iii) acombination of (i) and (ii); any of (i)-(iii) implement the specifictechniques set forth herein.

Techniques of the present invention can provide substantial beneficialtechnical effects; e.g. dynamic reconfiguration, including switching areservoir model, during a coupled workflow. Thus, an illustrativeembodiment of the invention may advantageously help professionals ininterdisciplinary areas making decisions in oil exploration analysis.

These and other features and advantages of the present invention willbecome apparent from the following detailed description of illustrativeembodiments thereof, which is to be read in connection with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart showing a conventional one-way coupling algorithm;

FIG. 2 is a flowchart showing a conventional two-way coupling algorithm;

FIG. 3 is a flowchart showing a dynamic two-way coupling algorithm,according to an aspect of the invention;

FIG. 4 shows a combined block diagram and flow chart, according to anaspect of the invention;

FIG. 5 shows another combined block diagram and flow chart, according toan aspect of the invention;

FIG. 6 is a flowchart showing a classification algorithm according to anaspect of the invention;

FIG. 7 shows an exemplary strain-stress curve suitable for use with anillustrative embodiment of the present invention;

FIG. 8 shows a plurality of exemplary strain-stress curves suitable foruse with an illustrative embodiment of the present invention;

FIG. 9 shows exemplary training data for geomaterials, and correspondingoutput from a geomechanical simulator, according to an aspect of theinvention;

FIG. 10 depicts a computer system that may be useful in implementing oneor more aspects and/or elements of the invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Known coupling algorithms include one-way coupling (1WC) and two-waycoupling (2WC). FIG. 1 is a flowchart showing a conventional one-waycoupling (1WC) algorithm 100. Algorithm 100 begins with the selection ofa reservoir model in step 110.

This selection may be made manually by a user or automatically by acomputer. As discussed above, the selected reservoir model is used forall subsequent simulation, including reservoir simulation 120 andgeomechanical simulation 140. The reservoir model may be selected from agroup including single porosity (SP), dual porosity (DP), and dualpermeability (DK). In step 120, the selected reservoir model is used bya reservoir simulator to analyze fluid mechanics thereby computingpressure, temperature, and saturation (PTS) values 130. The PTS values130, as well as the selected reservoir model 110, are used in step 140by a geomechanical simulator to analyze solid fracture mechanics therebycomputing stress, strain, and displacement values 150. The values 130computed by the reservoir simulator and the values 150 computed by thegeomechanical simulator are output in step 170 and algorithm 100terminates.

FIG. 2 is a flowchart showing a conventional two-way coupling (2WC)algorithm 200. Steps 210, 220, 230, 240, 250 and 270 in algorithm 200respectively correspond to steps 110, 120, 130, 140, 150 and 170 inalgorithm 100, discussed above with reference to FIG. 1. Algorithm 200begins in step 210 with the selection of a reservoir model. Thisselection may be made manually by a user or automatically by a computer.As discussed above, the selected reservoir model is used for allsubsequent simulation, including reservoir simulation 220 andgeomechanical simulation 240. The reservoir model may be selected from agroup including single porosity (SP), dual porosity (DP), and dualpermeability (DK). In step 220, the selected reservoir model is used bya reservoir simulator to analyze fluid mechanics thereby computingpressure, temperature, and saturation (PTS) values 230. The PTS values230, as well as the selected reservoir model 210, are used in step 240by a geomechanical simulator to analyze solid fracture mechanics therebycomputing stress, strain, and displacement values 250.

In step 260, a determination is made as to whether to repeat the abovesteps. This determination may be made manually by a user orautomatically by a computer. For example, the steps may be repeated apredetermined number of times. If the determination in step 260 is no,then the values 230 computed by the reservoir simulator and the values250 computed by the geomechanical simulator are output in step 270 andalgorithm 200 terminates. If the determination in step 260 is yes, thencoupling parameters (e.g., permeability and/or porosity) are updated instep 290, and the algorithm is repeated beginning with step 220 usingthe updated coupling parameters. It is important to note that step 210is not repeated. Once a reservoir model is selected in step 210, itcannot subsequently be changed in algorithm 200.

FIG. 3 is a flowchart showing a dynamic two-way coupling algorithm 300according to an aspect of the invention. Steps 310, 320, 330, 340, 350,360, 370 and 390 in algorithm 300 respectively correspond to steps 210,220, 230, 240, 250, 260, 270 and 290 in algorithm 200, discussed abovewith reference to FIG. 2. Steps 310, 320, 330, 340, 350 and 370 inalgorithm 300 also correspond respectively to steps 110, 120, 130, 140,150 and 170 in algorithm 100, discussed above with reference to FIG. 1.Algorithm 300 begins in step 310 with the initial selection of areservoir model.

The reservoir model may be selected from a group including singleporosity (SP), dual porosity (DP), and dual permeability (DK). In step320, the reservoir model, and potentially input from one or moresensors, is used by a reservoir simulator to analyze fluid mechanicsthereby computing pressure, temperature, and saturation (PTS) values330. The PTS values 330, as well as the reservoir model 310, are used instep 340 by a geomechanical simulator to analyze solid fracturemechanics thereby computing stress, strain, and displacement values 350.

In step 360, a determination is made as to whether to repeat the abovesteps. This determination may be made manually by a user orautomatically by a computer. For example, the steps may be repeated apredetermined number of times. If the determination in step 360 is no,then the values 330 computed by the reservoir simulator and the values350 computed by the geomechanical simulator are output in step 370, andmay be used to control hardware, and algorithm 300 terminates.

If the determination in step 360 is yes, then algorithm 300 proceeds tostep 380, which does not correspond to any step in algorithm 100 oralgorithm 200. In step 380, a machine learning classifier analyzes theoutput 350 from geomechanical simulation 340 and updates the reservoirmodel. For example, the machine learning classifier may select adifferent type of reservoir model (e.g., “dual porosity” instead of“single porosity”) than that which was previously selected, e.g., instep 310 and/or a previous iteration of step 380. The machine learningclassifier may implement the new selection automatically, or theselection may be presented to a user for confirmation prior toimplementation. Thus, algorithm 300 advantageously facilitates dynamicreconfiguration and switching a reservoir model during the workflow.Coupling parameters (e.g., permeability and/or porosity) are thenupdated in step 390, and the algorithm is repeated beginning with step320 using the updated reservoir model and coupling parameters.

FIG. 4 shows a combined block diagram and flow chart, according to anaspect of the invention. System 400 in FIG. 4 includes simulationsubsystem 401 and knowledge subsystem 402. Simulation subsystem 401implements an algorithm similar to algorithm 300 discussed above withreference to FIG. 3. Generally, elements 410, 420, 429, 430, 439, 440and 445 of simulation subsystem 401 respectively correspond to steps310, 320, 330, 340, 350, 360 and 370 shown in FIG. 3.

Within simulation subsystem 401, a reservoir model 419 is initiallychosen in 410, e.g., from a group including single porosity (SP), dualporosity (DP), and dual permeability (DK). Reservoir simulator 420 usesthe reservoir model 419 chosen in 410, and potentially input from one ormore sensors, to analyze fluid mechanics thereby computing pressure,temperature, and saturation (PTS) values 429, which are input togeomechanical simulator 430. Geomechanical simulator 430 analyzes solidfracture mechanics thereby computing output values 439 (e.g., stress,strain, and/or displacement).

Next, a determination is made at module 440 as to whether to repeat theabove steps. This determination may be made manually by a user orautomatically by a computer. Thus, module 440 may optionally include auser interface. For example, the steps may be repeated a predeterminednumber of times. If the determination at module 440 is no 441, then thevalues 439 computed by the geomechanical simulator 430 and the values429 computed by the reservoir simulator 420 are output 445, and may beused to control hardware, and the operation of simulation subsystem 401terminates. If the determination at module 440 is yes 442, thensubsystem 401 goes to Machine Learning Classifier module 480 andupdating module 490, which provides the updated model and couplingparameters 499 to reservoir simulator 420, before repeating the abovesteps of reservoir simulation 420 with output 429 and geomechanicalsimulation 430 with output 439.

If the determination at module 440 is yes 442 geomechanical simulatoroutput 439 is input to machine learning classifier 480 within simulationsubsystem 401. Machine learning classifier 480 includes fractureclassifier 483 and model switcher 487. Fracture classifier 483 appliesmachine learning techniques to the output values 439 from geomechanicalsimulator 430. As further discussed below, different geomaterials arecharacterized by mechanical properties and fracture behaviors, such asstress-strain curves. Fracture classifier 483 can then properties of aspecific geomaterial to analyze the results 439 of the geomechanicalsimulator 430 to determine whether fractures are present, moving and/orconnected.

Based on determinations 485 by fracture classifier 483, model switcher487 can determine whether the reservoir model is appropriate or needs tobe updated. The reservoir model may be selected from a group includingsingle porosity (SP), dual porosity (DP), and dual permeability (DK).The model switcher 487 may implement the new selection automatically, orthe selection may be presented to a user for confirmation prior toimplementation. Thus, model switcher 487 may optionally include a userinterface.

Model switcher 487 then provides the reservoir model 489, which may ormay not have been updated, and this model along with coupling parameters(permeability and/or porosity) are updated in 490 and provided 499 toreservoir simulator 420. Reservoir simulator 420 and/or geomechanicalsimulator 430 may optionally be implemented using commercially availablesoftware known to one skilled in the art.

In addition to simulation subsystem 401, system 400 includes knowledgesubsystem 402. Knowledge subsystem 402 includes knowledge registration450, knowledge base 460, and fracture classifier training 470. Fractureclassifier training 470 updates 479 fracture classifier 483 based ondata from knowledge base 460.

Knowledge base 460 is a database which may include data from either, orboth, knowledge registration module 450 (input 461) and/or modelswitcher 487 (input 462). It should be noted that input 462 is shown inFIG. 4 as being received specifically from model switcher 487, input 462may be received more generally from machine learning classifier 480, andstill more generally from simulation subsystem 401.

Input 461 involves registration 450 of knowledge (e.g., rules,properties, data, etc.) 459 found in research literature 455 thatprovide guidance as to which reservoir model 489 is appropriate for agiven stress, strain, and geomaterial. Input 461 may be used toinitialize knowledge base 460 prior to execution of simulation subsystem401. Input 461 may include updates to knowledge base 460 provided duringand/or after execution of simulation subsystem 401.

Input 462 involves registration of the decisions 489 made by modelswitcher 487 (and/or decisions 485 made by fracture classifier 483), aswell as the corresponding values 439 from geomechanical simulation 430(and/or output 485 from fracture classifier 483) upon which decisions489 were based. Input 462 may be used to iteratively update knowledgebase 460 during and/or after each execution of simulation subsystem 401,and more specifically each execution of machine learning classifier 480.

As previously discussed, model switcher 487 may involve providing arecommendation regarding a reservoir simulation, possibly accompanied byreasoning and/or justification, for evaluation by a human expert priorto making a final decision. If knowledge base 460 is of poor quality(e.g., not enough training data has been accumulated), then modelswitcher 487 may be programmed to give no recommendation and thus modelchoice would rely entirely on expert domain knowledge. Conversely, ifknowledge base 460 is of high quality (e.g., sufficient training datahas been accumulated), the model choice may be fully automated, suchthat the user would not have to rely on expert knowledge.

FIG. 5 shows a combined block diagram and flow chart, according to anaspect of the invention. The elements shown in FIG. 5 are similar to,but not necessarily identical to, a subset of the elements within system400 discussed above with reference to FIG. 4. Generally, classificationphase 501 corresponds to a subset of simulation system 401, and elements510, 519, 523, 525 and 527 respectively correspond to elements 430, 439,483, 485 and 487 shown in FIG. 4.

Training phase 502 corresponds generally to knowledge subsystem 402.Elements 561, 562 and 579 respectively correspond to elements 469, 462and 479 shown in FIG. 4. Element 554 corresponds generally to element460 shown in FIG. 4, and includes research in literature for canonicalstudies of the mechanical properties and/or fracture behavior of variousgeomaterials. Element 564 corresponds generally to element 470 shown inFIG. 4, and includes characterization of stress-strain curves stratifiedby geomaterial.

Accordingly, one or more embodiments of the present invention mayprovide a cognitive system that analyzes historical geomechanical datato create a classifier capable of making automated decisions usingoutput from a geomechanical simulator as classifier input. One or moreembodiments may include automatic post-processing of geomechanicalsimulation results by the classifier, including analysis ofdisplacements, stresses and strain to predict the best model forreservoir simulation based on fracture mechanics in the geomaterial.

Results from a geomechanical simulator are continuously analyzed inorder to determine and/or reevaluate the appropriateness of theunderlying reservoir model. A machine learning classifier is applied togeomechanical simulator output in order to determine whether fracturesare present, whether fractures are moving, and whether fractures areconnected. The answers to these questions determines which reservoirmodel is most appropriate.

Single porosity modeling, involves use of a standard reservoir simulatorand modeling of a fracture and matrix explicitly. Single porosity modelsare suitable for static scenarios (i.e., no moving fractures). Naturallyfractured reservoirs are characterized by the presence of two distincttypes of porous media: matrix and fracture. Dual-porosity models areapplied to highly fractured media where flow occurs between connectedfractures (high permeability) and no flow occurs between matrix blocks.In dual permeability models, fluid flow is assumed to happen not onlybetween connected fractures, but can occur between matrix blocks too.This happens mostly in non-connected fracture settings (lowpermeability).

FIG. 6 is a flowchart showing a classification algorithm 600 accordingto an aspect of the invention. Algorithm 600 may be implemented by aclassifier similar to 420 in FIG. 4 and/or may be implemented inconnection with step 380 in FIG. 3. Elements 619, 623 and 627 in FIG. 6respectively correspond to elements 519, 523 and 527 in FIG. 5, as wellas elements 419, 423 and 427 in FIG. 4.

Fracture classifier 623 obtains geomechanical simulation results 619.Based on these results, fracture classifier 623 detects whether one ormore moving fractures are present 633. If no moving fractures arepresent 641, a single porosity (SP) model 651 is output by 627. If oneor more moving fractures are present 635, fracture classifier 623detects whether the moving fractures are connected 637. If the movingfractures are connected 642, a dual porosity (DP) model 652 is output by627. If the moving fractures are not connected 643, a dual permeability(DK) model 653 is output by 627.

FIG. 7 shows an exemplary strain-stress curve suitable for use with anillustrative embodiment of the present invention. FIG. 7 shows strain c,and more specifically axial strain E_(a), along the x-axis 701. AlthoughFIG. 7 does not indicate units, strain c may be measured in, forexample, hundreds of microinches per inch (10² μin/in). FIG. 7 showsstress σ, and more specifically deviatoric stress σ_(d), along they-axis 702. Although FIG. 7 does not indicate units, stress σ may bemeasured in, for example, megapascal (MPa), kilopascal (kPa), bar,thousands of pounds per square inch (10³ psi).

The strain-stress curve shown in FIG. 7 has a peak 718, which is thestrain at which the stress is at a global maximum. The curve also has apre-peak area 710, which includes all strains less than peak 718, and apost-peak area 720, which includes all strains greater than peak 718. Inthe curve shown in FIG. 7, the pre-peak area 710 is characterized solelyby hardening 714, in which the stress increases as strain increases. Thepost-peak area 720, on the other hand, includes softening 724, in whichthe stress decreases as strain increases, followed by residual area 726,in which the stress remains substantially constant as strain increases,and finally failure 728.

Different geomaterials will have different strain-stress curves. Forexample, Indiana limestone and Solenhofen limestone will have differentstrain-stress curves.

Indeed, different geomaterials may exhibit very different post-peakbehaviors, as shown in FIG. 8. FIG. 8 shows a plurality of strain-stresscurves, which may be associated with different materials. Strain c is onthe x-axis 801, and stress σ is on the y-axis 802. For simplicity, it isassumed that the pre-peak area 810 and peak 818 of each of the curves isidentical; note that this unlikely to be the case where the curves areassociated with different materials.

822 shows a post-peak area for a brittle material in which failure(e.g., 728 in FIG. 7) occurs at peak 818 (corresponding to 718 in FIG.7). 824 shows a post-peak area for a strain-softening material whichexhibits behavior similar to that discussed above with reference to 724in FIG. 7: the stress decreases as strain increases. 826 shows apost-peak area for an elastic plastic material in which the stressremains substantially constant as strain increases. 828 shows apost-peak area for a strain-hardening material in which the stressincreases as strain increases.

Using fracture mechanics theory, a strain-stress curve, such as thatshown in FIG. 7, can be used to determine fracture behavior:

-   -   1. If the current (strain, stress) is in the pre-peak area, then        there are no fractures    -   2. If the current (strain, stress) is in the post-peak area,        then the material is fractured    -   3. A steeper decline at the post-peak area indicates a more        fragile geomaterial

Using the algorithm shown in FIG. 6, an appropriate reservoir model cantherefore be selected as follows:

-   -   1. If the current (strain, stress) is in the pre-peak area (but        not near the peak), then material fractures are not moving, and        a single porosity model (SP) should be used    -   2. If the current (strain, stress) is at or near the peak (e.g.,        between the pre-peak area and post-peak area), then material        fractures are moving and connected, and a dual porosity model        (DP) should be used    -   3. If the current (strain, stress) is in the post-peak area (but        not near the peak), then material fractures are moving but not        connected, and a dual permeability model (DK) should be used

Thus, the objective of the training phase 502 in FIG. 5 and/or knowledgesubsystem 402 in FIG. 4 is to characterize strain-stress curves forgeomaterials existing in the literature. The objective of theclassification phase 501 in FIG. 5 and/or machine learning classifier420 in FIG. 4 is to compare (stress, strain) data output by thegeomechanical simulator (i.e., current data) with the aforementionedclassified stress-strain curves (i.e., historical data) to determine(i.e., classify) fracture behavior.

With reference to FIG. 4, knowledge registration 450 involvesregistration 461 in knowledge base 460 (corresponding to training data554 in FIG. 5) of stress-strain curves for different geomaterialsexisting in the literature 455. Several stress-strain curves may beregistered for the same geomaterial to reflect uncertainty in empiricalstudies. This may involve the complete set of all stress-strain curvesfound in the literature. The registered stress-strain curves may furtherbe characterized by the mean and variability in the stress-strain curvesfor at least a given geomaterial. For example, the average stress-straincurve may be characterized for the geomaterial along withstandard-deviations to account for variations across stress-straincurves within the same geomaterial. More generally, the characterizationmay involve a complete characterization of the distribution ofstress-strain curves for at least a given geomaterial. This type ofcharacterization is useful for determining how many standard deviationsnew (stress, strain) observations from geomechanical simulator 430 arefrom the average stress-strain curve of the given geomaterial and canthus help with the model classification. In an illustrative embodiment,the curves can be registered into a data array comprising columns suchas (stress, strain, geomaterial, reservoir type, etc.) where each datarow corresponds to a distinct (stress, strain) observation of the curve.This registered data then serves as training data 460 that is trained in470 for fracture classifier 483.

FIG. 9 shows exemplary training data for geomaterials, and correspondingoutput from a geomechanical simulator, according to an aspect of theinvention. In an illustrative embodiment, the rules discussed above canbe applied by a fracture classifier comparing (strain, stress) pointsoutput from a geomechanical simulator observed at reservoir grid cellsfor a given geomaterial to training data curves stratified by the samegeomaterial in order to compute a fracture quantification, i.e., apercentage (P) of points with fractures, which is the percentage ofobserved points which are in the pre-peak area of the training datacurves:

-   -   1. If P<40% then material fractures are not moving, and a single        porosity model (SP) should be used    -   2. If 40% ≤P≤60% then material fractures are moving and        connected, and a dual porosity model (DP) should be used    -   3. If P>60% then material fractures are moving but not        connected, and a dual permeability model (DK) should be used

The fracture classifier is a model/calculator (f) that is capable ofpredicting a suitable reservoir simulation model (y) from ageomechanical simulation result (x): y=f(x). In some embodiments, thechoice of a reservoir model (y) may be an expert decision by a userbased on domain knowledge as well as geomechanical simulation data (x),such as when the fracture classifier has not yet been sufficientlytrained. With reference to FIG. 4, upon running simulation subsystem401, and more specifically machine learning classifier 480, thegeomechanical simulation results (x) and the corresponding decision asto which reservoir model to use (y) can be registered 462 into knowledgebase 460. For example, the data may be registered into a data arraycomprising columns: (strain, stress, geomaterial, reservoir type, modelchosen, etc. . . . )

After multiple uses of the simulation subsystem 401, the knowledge base460 will accumulate training data pairs (x₁, y₁), (x₂, y₂), (x₃, y₃) . .. wherein (x₁, y₁) denotes the geomechanical simulation results andcorresponding model choice at the i-th use of the simulation subsystem401. This data can then be used to train the fracture classifier 420using conventional machine learning classification algorithms.

An illustrative embodiment of the invention may attempt to fullycharacterize stress-strain relationships in different geomaterials usingmachine learning. Given a new reservoir with calculated (stress-strain)observations, the fracture classifier can consult the catalog oftraining results (canonical studies) and predict whether a fracture ismoving or not moving at critical regions, e.g. at closed fractures atproduction wells. An example decision by the fracture classifier underthese circumstances may indicate that the fracture is most likelymoving, with 70% probability, and may optionally be accompanied by avisualization of the fracture quantification results.

In conclusion, an illustrative embodiment of the invention includesmachine learning based coupling in a research model fornaturally-fractured reservoirs to help with analyzing fractures anddamage in the hydrocarbon reservoir. An illustrative embodiment includescreating a library for measuring a plurality of damaged structuresformed in geomaterials using solid mechanical methodology that isconcerned with the stress, deformation, and fracture in rocks. Materialfracture and damage identification can be provided as samples fortraining in order to have a Machine learning algorithm (ML) basedstress-strain relationship for a geomaterial. Experimental laboratory ornumerical test of the behavior of rocks under different loading patternsis conducted, and then the obtained data are used to train a ML model.If the training data contains sufficient relevant information, thetrained ML model is able to characterize the stress-strain relationshipof any geomaterial.

Classification of underlying physics controlling geomaterial behaviorincludes identification of behaviors before, during, and after thefailure mechanism is initiated. The material physics processes mayincorporate, for example, brittle fracture, elastic response, plasticresponse, displacement movements controlling plastic response,interactions governing fracture, and any combination thereof. Anillustrative embodiment of the present invention includes blending ofphysical models with a machine-learning model that applies historicalengineering experience from experimental laboratory studies andcomputational mechanics analysis.

One or more embodiments of the invention, or elements thereof, can beimplemented, at least in part, in the form of an apparatus including amemory and at least one processor that is coupled to the memory andoperative to perform exemplary method steps.

One or more embodiments can make use of software running on a generalpurpose computer or workstation. With reference to FIG. 10, such animplementation might employ, for example, a processor 1002, a memory1004, and an input/output interface formed, for example, by a display1006 and a keyboard 1008. The term “processor” as used herein isintended to include any processing device, such as, for example, onethat includes a CPU (central processing unit) and/or other forms ofprocessing circuitry. Further, the term “processor” may refer to morethan one individual processor. The term “memory” is intended to includememory associated with a processor or CPU, such as, for example, RAM(random access memory), ROM (read only memory), a fixed memory device(for example, hard drive), a removable memory device (for example,diskette), a flash memory and the like. In addition, the phrase“input/output interface” as used herein, is intended to include, forexample, one or more mechanisms for inputting data to the processingunit (for example, mouse), and one or more mechanisms for providingresults associated with the processing unit (for example, printer). Theprocessor 1002, memory 1004, and input/output interface such as display1006 and keyboard 1008 can be interconnected, for example, via bus 1010as part of a data processing unit 1012. Suitable interconnections, forexample via bus 1010, can also be provided to a network interface 1014,such as a network card, which can be provided to interface with acomputer network, and to a media interface 1016, such as a diskette orCD-ROM drive, which can be provided to interface with media 1018.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in one or more of the associated memory devices (for example,ROM, fixed or removable memory) and, when ready to be utilized, loadedin part or in whole (for example, into RAM) and implemented by a CPU.Such software could include, but is not limited to, firmware, residentsoftware, microcode, and the like.

A data processing system suitable for storing and/or executing programcode will include at least one processor 1002 coupled directly orindirectly to memory elements 1004 through a system bus 1010. The memoryelements can include local memory employed during actual implementationof the program code, bulk storage, and cache memories which providetemporary storage of at least some program code in order to reduce thenumber of times code must be retrieved from bulk storage duringimplementation.

Input/output or I/O devices (including but not limited to keyboards1008, displays 1006, pointing devices, and the like) can be coupled tothe system either directly (such as via bus 1010) or through interveningI/O controllers (omitted for clarity).

Network adapters such as network interface 1014 may also be coupled tothe system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Modems, cable modem andEthernet cards are just a few of the currently available types ofnetwork adapters.

As used herein, including the claims, a “server” includes a physicaldata processing system (for example, system 1012 as shown in FIG. 12)running a server program. It will be understood that such a physicalserver may or may not include a display and keyboard.

It should be noted that any of the methods described herein can includean additional step of providing a system comprising distinct softwaremodules embodied on a computer readable storage medium; the modules caninclude, for example, any or all of the elements depicted in the blockdiagrams or other figures and/or described herein. The method steps canthen be carried out using the distinct software modules and/orsub-modules of the system, as described above, executing on one or morehardware processors 1002. Further, a computer program product caninclude a computer-readable storage medium with code adapted to beimplemented to carry out one or more method steps described herein,including the provision of the system with the distinct softwaremodules.

Exemplary System and Article of Manufacture Details

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. An apparatus for controlling wellhead equipment in a fluid reservoir, comprising: a memory; and at least one processor coupled with the memory, the processor operative to implement at least one machine learning classifier, the machine classifier being operative to: obtain one or more recent results from at least one geomechanical simulation; compare the recent results to stored historical data; based on the comparing, decide at least one reservoir model for use by at least one reservoir simulation; and control the wellhead equipment using the results from the at least one geomechanical simulation and from the at least one reservoir simulation, wherein: the one or more recent results from the at least one geomechanical simulation comprise at least one point, each point comprising at least one strain value and at least one stress value for at least a given geomaterial; and the stored historical data comprises at least one stress-strain curve for at least the given geomaterial.
 2. The apparatus of claim 1, wherein the stored historical data comprises a plurality of stress-strain curves for the given geomaterial.
 3. The apparatus of claim 2, wherein the stored historical data further comprises: an average stress-strain curve for the given geomaterial; at least one stress-strain curve based at least in part on: the average stress-strain curve for the given geomaterial; and a standard deviation of the plurality of stress-strain curves for the given geomaterial.
 4. The apparatus of claim 1, wherein: the at least one point in the one or more recent results from the at least one geomechanical simulation comprises a current point for at least a given geomaterial; the current point for the given geomaterial comprises a current stress value and a current strain value; and the machine classifier is operative to decide by at least one of: selecting at least one single porosity model if the current point is within a pre-peak area of the at least one stress-strain curve for at least the given geomaterial; selecting at least one dual porosity model if the current point is within a peak area of the at least one stress-strain curve for at least the given geomaterial; and selecting at least one dual permeability model if the current point is within a post-peak area of the at least one stress-strain curve for at least the given geomaterial.
 5. The apparatus of claim 1, wherein: the at least one point in the one or more recent results from the at least one geomechanical simulation comprises a plurality of points for at least the given geomaterial from the at least one geomechanical simulation; each of the plurality of points for the given geomaterial comprises a respective stress value and a respective strain value; and the machine classifier is operative to compare by computing a proportion of the plurality of points which are within a pre-peak area of the at least one stress-strain curve for at least the given geomaterial.
 6. The apparatus of claim 5, wherein the plurality of points for at least the given geomaterial from the at least one geomechanical simulation a current point for at least a given geomaterial comprises a current point and at least one prior point.
 7. The apparatus of claim 5, wherein: the machine classifier is further operative to compare by determining at least one characteristic of at least one fracture in at least the given geomaterial based at least in part on the computed proportion; and the machine classifier is operative to decide by deciding the at least one reservoir model based at least in part on the determined characteristic of the fracture.
 8. The apparatus of claim 5, wherein the machine classifier is operative to decide by at least one of: selecting at least one single porosity model if the proportion is less than a first value; selecting at least one dual porosity model if the proportion is between the first value and a second value, the second value being greater than the first value; and selecting at least one dual permeability model if the proportion is greater than the second value.
 9. The apparatus of claim 8, wherein the first value is about forty per cent, and wherein the second value is about sixty percent.
 10. The apparatus of claim 1, wherein: the stored historical data comprises a plurality of stress-strain curves comprising at least one stress-strain curve for each of a plurality of geomaterials comprising at least the given geomaterial.
 11. An apparatus for controlling wellhead equipment in a fluid reservoir, comprising: a memory; and at least one processor coupled with the memory, the processor operative to implement at least one machine learning classifier, the machine classifier being operative to: obtain one or more recent results from at least one geomechanical simulation; compare the recent results to stored historical data; based on the comparing, decide at least one reservoir model for use by at least one reservoir simulation; and control the wellhead equipment using the results from the at least one geomechanical simulation and from the at least one reservoir simulation, wherein: the machine classifier is operative to compare by determining at least one characteristic of at least one fracture in at least one geomaterial; and the machine classifier is operative to decide on the at least one reservoir model based at least in part on the determined characteristic of the fracture.
 12. The apparatus of claim 11, wherein the characteristic of the fracture comprise at least one of presence, movement, and connectedness.
 13. The apparatus of claim 12, wherein the machine classifier is operative to decide by at least one of: selecting at least one single porosity model if the fracture is not present or is not moving; selecting at least one dual porosity model if the fracture is moving and connected; and selecting at least one dual permeability model if the fracture is moving and not connected.
 14. A non-transitory computer readable medium comprising computer executable instructions which when executed by a computer cause the computer to perform a method for controlling wellhead equipment in a fluid reservoir, the method comprising: implementing at least one machine learning classifier; obtaining, with the implemented machine learning classifier, one or more recent results from at least one geomechanical simulation; comparing, with the implemented machine learning classifier, the recent results to stored historical data; based on the comparing, deciding, with the implemented machine learning classifier, at least one reservoir model for use by at least one reservoir simulation; and controlling, with the implemented machine learning classifier, the wellhead equipment using the results from the at least one geomechanical simulation and from the at least one reservoir simulation, wherein: the one or more recent results from the at least one geomechanical simulation comprise at least one point, each point comprising at least one strain value and at least one stress value for at least a given geomaterial; and the stored historical data comprises at least one stress-strain curve for at least the given geomaterial.
 15. The non-transitory computer readable medium of claim 14, wherein the stored historical data comprises a plurality of stress-strain curves for the given geomaterial.
 16. The non-transitory computer readable medium of claim 15, wherein the stored historical data further comprises: an average stress-strain curve for the given geomaterial; at least one stress-strain curve based at least in part on: the average stress-strain curve for the given geomaterial; and a standard deviation of the plurality of stress-strain curves for the given geomaterial.
 17. The non-transitory computer readable medium of claim 14, wherein: the at least one point in the one or more recent results from the at least one geomechanical simulation comprises a current point for at least a given geomaterial; the current point for the given geomaterial comprises a current stress value and a current strain value; and the machine learning classifier is operative to decide by at least one of: selecting at least one single porosity model if the current point is within a pre-peak area of the at least one stress-strain curve for at least the given geomaterial; selecting at least one dual porosity model if the current point is within a peak area of the at least one stress-strain curve for at least the given geomaterial; and selecting at least one dual permeability model if the current point is within a post-peak area of the at least one stress-strain curve for at least the given geomaterial.
 18. The non-transitory computer readable medium of claim 14, wherein: the at least one point in the one or more recent results from the at least one geomechanical simulation comprises a plurality of points for at least the given geomaterial from the at least one geomechanical simulation; each of the plurality of points for the given geomaterial comprises a respective stress value and a respective strain value; and the machine learning classifier is operative to compare by computing a proportion of the plurality of points which are within a pre-peak area of the at least one stress-strain curve for at least the given geomaterial.
 19. The non-transitory computer readable medium of claim 18, wherein the plurality of points for at least the given geomaterial from the at least one geomechanical simulation a current point for at least a given geomaterial comprises a current point and at least one prior point.
 20. The non-transitory computer readable medium of claim 18, wherein: the machine learning classifier is further operative to compare by determining at least one characteristic of at least one fracture in at least the given geomaterial based at least in part on the computed proportion; and the machine learning classifier is operative to decide by deciding the at least one reservoir model based at least in part on the determined characteristic of the fracture. 